An approach for unsupervised contextual anomaly detection and characterization

Outlier detection has been widely explored and applied to different real-world problems. However, outlier characterization that consists in finding and understanding the outlying aspects of the anomalous observations is still challenging. In this paper, we present a new approach to simultaneously detect subspace outliers and characterize them. We introduce the Dimension-wise Local Outlier Factor (DLOF) function to quantify the degree of outlierness of the data points in each feature dimension. The obtained DLOFs are used in an outlier ensemble so as to detect and rank the anomalous points. Subsequently, the same DLOFs are analyzed in order to characterize the detected outliers with their relevant subspace and their same-type anomalies. Experiments on various datasets show the efficacy of our method. Indeed, we demonstrate through an experimental evaluation that the proposed approach is competitive compared to the existing solutions in terms of both detection and characterization accuracy.